def get_bubble(exp_params, sim, crit_thresh, crit_rad):
path2RESTsim = rest_sim_location(*exp_params, sim)
path2CLEANsim = clean_sim_location(*exp_params, sim)
bubble = np.load(path2CLEANsim)[0]
bubble = np.array([bubble[0]])
totbeta = np.load(path2RESTsim)[-1]
# bubble = multiply_bubble(bubble, lightc, phieq, totbeta, normal, nLat)
bubble = bubble[0]
# bubble = gaussian_filter(bubble, sigma=2, mode='nearest')
tcen, xcen = find_nucleation_center(bubble, phieq, crit_thresh, crit_rad)
bubble = bubble[max(tcen-nLat*2//3,0):tcen]
mn = np.mean(bubble)
bubble-= mn
return bubble, mn
def get_HT(array):
array = array - np.mean(array)
w = np.fft.fftfreq(len(array), d=1) * len(array)
FD = np.fft.fft(array, axis=0)
FD[w<=0.,:] = 0.
FD[w>0.,:] *= 2.
HD = np.fft.ifft(FD, axis=0)
return np.abs(HD)
def get_4T(array):
nT, nN = np.shape(array)
tw = np.fft.fftfreq(nT, d=1) * nT
xw = np.fft.fftfreq(nN, d=1) * nN
tFD = np.fft.fft(array, axis=0)
tFD[tw<0,:] = 0.
tFD[tw>0,:]*= 2.
tHD = np.fft.ifft(tFD, axis=0)
xFD = np.fft.fft(array, axis=1)
xFD[:,xw<0] = 0.
xFD[:,xw>0]*= 2.
xHD = np.fft.ifft(xFD, axis=1)
lFD = np.fft.fft(tHD, axis=1)
lFD[:,xw<0] = 0.
lFD[:,xw>0]*= 2.
lHD = np.fft.ifft(lFD, axis=1)
rFD = np.fft.fft(xHD, axis=0)
rFD[tw>0,:] = 0.
rFD[tw<0,:]*= 2.
rHD = np.fft.ifft(rFD, axis=0)
return np.abs(tHD), np.abs(xHD), np.abs(lHD), np.abs(rHD)
def get_cummul_power(freqsT, freqsX, PST, PSX):
# flat list of all frequencies where response is strong
all_frqT = flatten_comprehension2(savefrqT)
all_frqX = flatten_comprehension2(savefrqX)
all_psT = flatten_comprehension(savePST)
all_psX = flatten_comprehension(savePSX)
# cut duplicates
set_Tfreqs = np.sort(all_frqT)
set_Xfreqs = np.sort(all_frqX)
# add field responses to get relative importance of each mode
set_Ttotamps = np.zeros(len(set_Tfreqs))
for fi, frq in enumerate(set_Tfreqs):
inds = np.argwhere(np.abs(all_frqT)==frq).flatten()
set_Ttotamps[fi] = np.sum(all_psT[inds])
# and in X
set_Xtotamps = np.zeros(len(set_Xfreqs))
for fi, frq in enumerate(set_Xfreqs):
inds = np.argwhere(np.abs(all_frqX)==frq).flatten()
set_Xtotamps[fi] = np.sum(all_psX[inds])
return set_Tfreqs, set_Xfreqs, set_Ttotamps, set_Xtotamps
def get_osc_trajectory(array, extent):
# find maximum value in array
T, N = np.shape(array)
flattened = chain.from_iterable(array)
max_idx, max_val = max(enumerate(flattened), key=op.itemgetter(1))
row = max_idx // N
col = max_idx % N
# print(array[row][col] == max_val, max_val, array[row][col], row, col)
# ax[axis].plot(col, row, 'ro', ms=10)
# use as starting point for oscillon trajectory
# print(col, row)
maxLine = []
CCol = col
for rr in range(row)[::-1]:
if rr == row-1:
col = CCol
colmin = col - extent
colmax = col + extent+1
val = 0
for cc in range(colmin, colmax):
cc = cc%N
if array[rr][cc] > val:
val = array[rr][cc]
col = cc
maxLine.append(col)
maxLine = maxLine[::-1]
for rr in range(row, T):
if rr == row:
col = CCol
colmin = col - extent
colmax = col + extent+1
val = 0
for cc in range(colmin, colmax):
cc = cc%N
if array[rr][cc] > val:
val = array[rr][cc]
col = cc
maxLine.append(col)
return np.array(maxLine)
# average oscillon trajectories
def tolerant_mean(arrs):
lens = np.array([len(i) for i in arrs])
# print(lens.tolist())
arr = np.zeros((len(lens), np.max(lens)))
for ri, osc in enumerate(arrs):
arr[ri, :len(osc)] = osc
arr[arr == 0] = np.nan
return np.nanmean(arr, axis=0), np.nanstd(arr, axis=0)
def tolerant_mean2d(arrs):
lens = np.array([np.shape(i) for i in arrs])
lens1, lens2 = lens[:,0], lens[:,1]
arr = np.zeros((len(arrs), np.max(lens1), np.max(lens2)))
for ri, osc in enumerate(arrs):
n1, n2 = np.shape(osc)
arr[ri, :n1, :n2] = osc
arr[arr == 0.] = np.nan
return np.nanmean(arr, axis=0), np.nanstd(arr, axis=0)
def flatten_comprehension(matrix):
return np.array([item for row in matrix for item in row])
def flatten_comprehension2(matrix):
return np.array([np.abs(np.round(item,10)) for row in matrix for item in row])
